--- title: SynCamVideo-Dataset canonical_url: "https://www.modelscope.cn/datasets/KwaiVGI/SynCamVideo-Dataset" md_url: "https://www.modelscope.cn/datasets/KwaiVGI/SynCamVideo-Dataset.md" repository: KwaiVGI/SynCamVideo-Dataset last_updated: 2025-09-03 license: apache-2.0 storage_size: "42 GB" downloads: 177 stars: 1 --- # SynCamVideo-Dataset > SynCamVideo-Dataset - KwaiVGI 在 ModelScope 开源的数据集。📷 Dataset: SynCamVideo Dataset KwaiVGI/SynCamVideo-Dataset 是 ModelScope 魔搭社区上的数据集，存储大小 42 GB，采用 apache-2.0 许可。 - **Repository**: KwaiVGI/SynCamVideo-Dataset - **License**: apache-2.0 - **Storage size**: 42 GB - **Downloads**: 177 - **Stars**: 1 - **Last updated**: 2025-09-03 Source: https://www.modelscope.cn/datasets/KwaiVGI/SynCamVideo-Dataset --- [Github](https://github.com/KwaiVGI/SynCamMaster) [Project Page](https://jianhongbai.github.io/SynCamMaster/) [Paper](https://arxiv.org/abs/2412.07760) ## 📷 Dataset: SynCamVideo Dataset - __[2025.04.15]__: Release a new version of the SynCamVideo Dataset with improved quality and greater diversity. - __[2025.04.15]__: Please also check our [MultiCamVideo](https://huggingface.co/datasets/KwaiVGI/MultiCamVideo-Dataset) Dataset. ### 1. Dataset Introduction **TL;DR:** The SynCamVideo Dataset is a multi-camera synchronized video dataset rendered using Unreal Engine 5. It includes synchronized multi-camera videos and their corresponding camera poses. The SynCamVideo Dataset can be valuable in fields such as camera-controlled video generation, synchronized video production, and 3D/4D reconstruction. The camera is stationary in the SynCamVideo Dataset. If you require footage with moving cameras rather than stationary ones, please explore our [MultiCamVideo](https://huggingface.co/datasets/KwaiVGI/MultiCamVideo-Dataset) Dataset.
The SynCamVideo Dataset is a multi-camera synchronized video dataset rendered using Unreal Engine 5. It includes synchronized multi-camera videos and their corresponding camera poses. It consists of 3.4K different dynamic scenes, each captured by 10 cameras, resulting in a total of 34K videos. Each dynamic scene is composed of four elements: {3D environment, character, animation, camera}. Specifically, we use animation to drive the character and position the animated character within the 3D environment. Then, Time-synchronized cameras are set up to render the multi-camera video data.

Example Image

**3D Environment:** We collect 37 high-quality 3D environments assets from [Fab](https://www.fab.com). To minimize the domain gap between rendered data and real-world videos, we primarily select visually realistic 3D scenes, while choosing a few stylized or surreal 3D scenes as a supplement. To ensure data diversity, the selected scenes cover a variety of indoor and outdoor settings, such as city streets, shopping malls, cafes, office rooms, and the countryside. **Character:** We collect 66 different human 3D models as characters from [Fab](https://www.fab.com) and [Mixamo](https://www.mixamo.com). **Animation:** We collect 93 different animations from [Fab](https://www.fab.com) and [Mixamo](https://www.mixamo.com), including common actions such as waving, dancing, and cheering. We use these animations to drive the collected characters and create diverse datasets through various combinations. **Camera:** To enhance the diversity of the dataset, each camera is randomly sampled on a hemispherical surface centered around the character. ### 2. Statistics and Configurations Dataset Statistics: | Number of Dynamic Scenes | Camera per Scene | Total Videos | |:------------------------:|:----------------:|:------------:| | 3400 | 10 | 34,000 | Video Configurations: | Resolution | Frame Number | FPS | |:-----------:|:------------:|:------------------------:| | 1280x1280 | 81 | 15 | Note: You can use 'center crop' to adjust the video's aspect ratio to fit your video generation model, such as 16:9, 9:16, 4:3, or 3:4. Camera Configurations: | Focal Length | Aperture | Sensor Height | Sensor Width | |:-----------------------:|:------------------:|:-------------:|:------------:| | 24mm | 5.0 | 23.76mm | 23.76mm | ### 3. File Structure ``` SynCamVideo-Dataset ├── train │ └── f24_aperture5 │ ├── scene1 # one dynamic scene │ │ ├── videos │ │ │ ├── cam01.mp4 # synchronized 81-frame videos at 1280x1280 resolution │ │ │ ├── cam02.mp4 │ │ │ ├── ... │ │ │ └── cam10.mp4 │ │ └── cameras │ │ └── camera_extrinsics.json # 81-frame camera extrinsics of the 10 cameras │ ├── ... │ └── scene3400 └── val └── basic ├── videos │ ├── cam01.mp4 # example videos corresponding to the validation cameras │ ├── cam02.mp4 │ ├── ... │ └── cam10.mp4 └── cameras └── camera_extrinsics.json # 10 cameras for validation ``` ### 3. Useful scripts - Data Extraction ```bash tar -xzvf SynCamVideo-Dataset.tar.gz ``` - Camera Visualization ```python python vis_cam.py ```

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## Acknowledgments We thank Jinwen Cao, Yisong Guo, Haowen Ji, Jichao Wang, and Yi Wang from Kuaishou Technology for their invaluable help in constructing the SynCamVideo-Dataset. ## 🌟 Citation Please cite our paper if you find our dataset helpful. ``` @misc{bai2024syncammaster, title={SynCamMaster: Synchronizing Multi-Camera Video Generation from Diverse Viewpoints}, author={Jianhong Bai and Menghan Xia and Xintao Wang and Ziyang Yuan and Xiao Fu and Zuozhu Liu and Haoji Hu and Pengfei Wan and Di Zhang}, year={2024}, eprint={2412.07760}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2412.07760}, } ``` ## Contact [Jianhong Bai](https://jianhongbai.github.io/)